Halil Utku Unlu
Robotics Research (United States), New York University, Long Island University
Papers
4
Total Citations
24
H-Index
2
About
Halil Utku Unlu is a robotics researcher whose work lies at the intersection of deep learning, sensor fusion, and autonomous navigation. His most impactful contribution, a 2019 paper on sliding-window temporal attention for robust sensor modality fusion in UGV navigation (16 citations), introduced a novel neural architecture that significantly improves how autonomous vehicles process time-series data from multiple sensors in uncertain environments. Building on this foundation, Unlu has advanced safe motion planning with his 2024 work on Control Barrier Function-based algorithms for quadruped robots, enabling these systems to navigate unknown terrains while maintaining safety constraints. More recently, he has pushed the boundaries of zero-shot object goal navigation, exploring how foundation models can reliably select frontiers and achieve semantic understanding without task-specific training. His 2024 publications in this area demonstrate a commitment to making robots more adaptable and intelligent in real-world settings. Unlu’s research trajectory—from sensor fusion to safety-critical planning to semantic reasoning—reflects a systematic approach to solving the core challenges of autonomous navigation, establishing him as a promising voice in modern robotics.
Research Focus
Key Achievements
Top Papers
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